TY - JOUR
T1 - Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture
AU - Cho, Eunsang
AU - Jacobs, Jennifer M.
AU - Schroeder, Ronny
AU - Tuttle, Samuel E.
AU - Olheiser, Carrie
N1 - Funding Information:
We would like to thank the four anonymous reviewers and the RSE editorial team including Drs. Menghua Wang and Tim McVicar for taking their time to provide constructive comments that improve this paper. The authors gratefully acknowledge support from NASA Water Resources Applied Sciences Program ( NNX15AC47G ). We thank Simon Kraatz (UNH) for constructive discussions; Mike Cosh (USDA), Pedro Restrepo, Mike DeWeese, and Brian Connelly (NOAA NCRFC) for their comments at the early stage of this research through our NASA RRB project. We are grateful to all who contributed to the data sets used in this study. The airborne gamma radiation survey SM and SWE data are available from the NOAA NWS NOHRSC website ( http://www.nohrsc.noaa.gov/snowsurvey/ ). The SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 2 (ID: R16000) were downloaded from the Earth Observing System Data and Information System (EOSDIS) ( https://earthdata.nasa.gov/ ). The SSMIS brightness temperature data (Version 2) are freely available from the NASA National Snow and Ice Data Center website ( https://nsidc.org/data/NSIDC-0032 ). The GlobSnow SWE data are available at http://www.globsnow.info/swe/ . The in-situ SCAN SWE are also available from NRCS National Water and Climate Center ( https://www.wcc.nrcs.usda.gov ). The weekly ground snow survey SWE data are available on request from the USACE St. Paul District.
Funding Information:
We would like to thank the four anonymous reviewers and the RSE editorial team including Drs. Menghua Wang and Tim McVicar for taking their time to provide constructive comments that improve this paper. The authors gratefully acknowledge support from NASA Water Resources Applied Sciences Program (NNX15AC47G). We thank Simon Kraatz (UNH) for constructive discussions; Mike Cosh (USDA), Pedro Restrepo, Mike DeWeese, and Brian Connelly (NOAA NCRFC) for their comments at the early stage of this research through our NASA RRB project. We are grateful to all who contributed to the data sets used in this study. The airborne gamma radiation survey SM and SWE data are available from the NOAA NWS NOHRSC website (http://www.nohrsc.noaa.gov/snowsurvey/). The SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 2 (ID: R16000) were downloaded from the Earth Observing System Data and Information System (EOSDIS) (https://earthdata.nasa.gov/). The SSMIS brightness temperature data (Version 2) are freely available from the NASA National Snow and Ice Data Center website (https://nsidc.org/data/NSIDC-0032). The GlobSnow SWE data are available at http://www.globsnow.info/swe/. The in-situ SCAN SWE are also available from NRCS National Water and Climate Center (https://www.wcc.nrcs.usda.gov). The weekly ground snow survey SWE data are available on request from the USACE St. Paul District.
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2020/4
Y1 - 2020/4
N2 - Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m3/m3), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m3/m3) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m3/m3) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.71–0.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.65–0.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System.
AB - Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m3/m3), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m3/m3) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m3/m3) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.71–0.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.65–0.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System.
KW - AMSR2
KW - Airborne gamma radiation
KW - NLDAS-2
KW - SMAP
KW - SSMIS
KW - Snow water equivalent
KW - Soil moisture
UR - http://www.scopus.com/inward/record.url?scp=85079178138&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079178138&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2020.111668
DO - 10.1016/j.rse.2020.111668
M3 - Article
AN - SCOPUS:85079178138
SN - 0034-4257
VL - 240
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 111668
ER -